For many merchants in service industries, employees may rely on gratuities as part of their regular income. Often times, the value of a gratuity that is provided by a consumer may be heavily affected by the quality of service provided to the consumer. For instance, a consumer at a restaurant may leave a higher gratuity for their server if the server gets their order correct, promptly brings new drinks and refills, and regularly checks in to ensure that the consumer has everything they need. In many of these instances, the gratuity left to a service industry employee is thus highly tied to the level of service provided.
At the same time, when choosing a service provider, many consumers often rely on referrals and reviews of service providers. For example, a consumer selecting a restaurant may view ratings for restaurants and read the reviews of other patrons to decide on where to go. However, many consumers do not leave reviews for merchants that they visit. In addition, consumers may be more often motivated to leave reviews if they have a negative experience rather than a positive one, which may skew the ratings for a service provider. Thus, there is a need for a more improved criteria by which a consumer may gauge a service provider prior to visiting.
The value of gratuities left by consumers at a service provider may be beneficial in providing a consumer with a more accurate estimation of the level and quality of service provided at the service provider. However, such information is often unavailable to consumers. Therefore, there is a need for a technical solution whereby gratuity values for merchant transactions may be captured and used to determine index values by which the level and quality of service at a service provider may be estimated, particularly when benchmarked against the level and quality of service of other service providers.